COMP47350

Learning Outcomes:

On successful completion of this module the learner will be able to:

1. Understand the principles and the purposes of data analytics.
2. Use Python to retrieve and analyse real-world datasets.
3. Apply the process of data understanding and address data quality issues.
4. Use appropriate machine learning techniques for a given data analytics problem.
5. Design evaluation experiments for selecting the best predictive model for a given analytics problem.

Student Effort Hours:

Approaches to Teaching and Learning:

active/task-based learning;
lectures;
lab work;
enquiry & problem-based learning;
case-based learning;

If students are permitted to use generative AI tools in assignments, that will be indicated in the assignment specification.

Requirements, Exclusions and Recommendations

Learning Recommendations:

Prior experience with programming in Python and working with the object-oriented programming paradigm.

Module Requisites and Incompatibles

Incompatibles:
COMP41680 - Data Science in Python, COMP47670 - Data Science in Python (MD)


 

Assessment Strategy

 

Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

1. "Fundamentals of Machine Learning for Predictive Data Analytics", J Kelleher, B MacNamee, A, D'Arcy, MIT Press, 2015
https://mitpress.mit.edu/books/fundamentals-machine-learning-predictive-data-analytics
http://machinelearningbook.com

2. The WEKA book:
"Data Mining: Practical Machine Learning Tools and Techniques", Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal
http://cs.du.edu/~mitchell/mario_books/Data_Mining:_Practical_Machine_Learning_Tools_and_Techniques_-_2e_-_Witten_&_Frank.pdf
Official website: http://www.cs.waikato.ac.nz/ml/weka/book.html
"Data Mining", I Witten, E Frank, M. Hall.
Its a good read for introductory concepts and covers more material
than the above textbook.

3. "The Elements of Statistical Learning", Hastie, Tibshirani, Friedman
https://statweb.stanford.edu/~tibs/ElemStatLearn/

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.